Ki67 Index in Breast Cancer Tissue Using QuPath
Introduction
In this tutorial, we will demonstrate how to measure the Ki67 index in breast cancer tissue using QuPath. The Ki67 index is a crucial marker for evaluating cell proliferation, providing insights into tumor aggressiveness and aiding in treatment decisions.
Importing the Ki67-Stained Whole Slide Image
We will be working with a Ki67-stained whole-slide image (WSI) from the ACROBAT dataset. For demonstration purposes, we’ll use the image “0_KI67_val.tif” from the validation dataset “valid.zip”. You can import this image by following the steps outlined in our first article, “Introduction to QuPath.”
Viewing and Preparing the Image
Understanding H-DAB Staining
Our demonstration image uses H-DAB staining, a technique explained in our previous article. Here’s a brief recap:
- Hematoxylin (H) stains the nuclei blue or purple.
- DAB (Diaminobenzidine) stains the target antigen, in this case, Ki67, brown.
In Ki67 staining, DAB highlights Ki67-positive nuclei with a brown color, indicating actively proliferating cells. The intensity and distribution of the brown stain provide insights into the tumor cells’ proliferation rate.
Understanding KI67 index
The Ki67 index is widely used to measure tumor cell proliferation. In H-DAB-stained tissue, brown-stained nuclei represent Ki67-positive cells, while blue or purple nuclei represent non-proliferating cells. Before detecting and counting cells, it’s essential to identify the correct regions of interest (ROIs) and understand what to include in the analysis.
Tumor Cell Regions for Analysis
To accurately measure the Ki67 index, we must later focus on regions with identifiable tumor cells. KI67 index is not measured within the whole sample, but regions with clearly identifiable tumor cells, excluding non-tumor components like stroma or necrosis. Areas where Ki67 expression appears most prominent, often called “hotspots,” are ideal for counting. Avoid regions with clumped or indistinct cells, as this can lead to inaccurate results when using QuPath’s automated tools.
Calculating Ki67 Index
The standard approach involves counting at least 500 to 1,000 tumor cells in areas of high magnification, focusing on those with the highest proliferation. The Ki67 index is calculated as the percentage of Ki67-positive nuclei out of the total number of nuclei in the tumor region, using the formula:
For this tutorial:
- Focus on Tumor Regions with high concentration of brown-stained nuclei (= KI67 positive nuclei)
- Exclude the stromal regions, which are the lighter areas with few cells
Identifying and Selecting Regions of Interest (ROIs)
In the previous article “Tissue Segmentation Using a Pixel Classifier in QuPath” we covered how to differentiate between various tissue regions. As you proceed with this tutorial, ensure that you have already completed the tissue segmentation step.
Depending on your tissue sample, there are different approaches for moving forward. You can choose to apply the following steps across the entire tumor/epithelial area previously classified by the pixel classifier. In this case, both cell detection and Ki67-positive cell detection will be performed for the entire region. While this area may not represent a specific hotspot, you can later draw annotations to focus on specific subregions or hotspots. QuPath will automatically display the number of cells and Ki67-positive cells for each annotation you create within the broader tumor/epithelial area. Alternatively, if your tissue is large or a distinct hotspot is visually evident, focusing on that specific area can produce faster and potentially more accurate results, especially in terms of classification parameters like threshold adjustments. For this tutorial, we will begin by analyzing the entire tissue section.
Automatic Cell Detection and Counting
Before we can specifically count Ki67-positive cells, we first need to detect all cells in the selected region.
- Analyze > Cell Detection: Navigate to Analyze > Cell detection. This opens the cell detection configuration panel.
- Set Parameters:
- Detections Image > Optical Density Sum: This option helps to differentiate cells based on staining intensity.
- Threshold: Adjust the intensity threshold to distinguish cells from the background. A lower threshold will include more pixels as potential cells, while a higher threshold will be more selective.
- Cell Size (Minimum Area, Maximum Area): Define the size range of the cells by setting minimum and maximum area values to differentiate actual cells from other tissue components.
- Include/Exclude Cell Nucleus: Ensure to tick off this box to exclude the nucleus in the detection.
- Run Detection: Once you’ve set your parameters, click Run to execute the cell detection algorithm. QuPath will automatically mark detected cells based on the configured settings.
- Review Results: After detection, review the results. You can adjust the parameters and rerun the detection until you’re satisfied. Double-click on a nucleus to view its DAB OD mean value in the measurements panel, which can help finding a suitable threshold to detect all cells. For better visibility, you can turn the detections on and off by pressing “D” on the keyboard or using the tool from the toolbar.
- View Measurements: The number of detected cells in your selected annotation will be displayed in the Measurements tab on the bottom left.
